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The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.

Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.

Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.

This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

The use of energy storage devices helps the consumers to utilize the benefits and flexibilities brought by smart networks. One of the major energy storage solutions is using electric vehicle batteries. The purpose of this paper is to develop an optimal energy management strategy for a consumer connected to the power grid equipped with Vehicle-to-Home (V2H) power supply and renewable power generation unit (PV).

The problem of energy flow management is formulated and solved as an optimization problem using a linear programming model. The total energy cost of the consumer is optimized. The optimal values of decision variables are found using CPLEX solver.

The simulation results demonstrated that if the optimal decisions are made regarding the V2H operation and managing the produced power by solar panels then the total energy payments are significantly reduced.

The gap that the proposed model is trying to fill is the holistic determination of an optimal energy procurement portfolio by using various embedded resources in an optimal way. The contributions of this paper are in threefold as: first, the introduction of mobile storage devices with a periodical availability depending on driving schedules; second, offering a new business model for managing the generation of PV modules by considering the possibility of grid injection or self-consumption; third, considering Real Time Pricing in the suggested formulation.

The researchers and policy makers worldwide have proposed many ideas for smart cities and homes in urban areas. The extensive work done for urban smart homes neglects the unique constraints of homes at remote mountain tops and deserts and rural village homes. The purpose of this paper is to propose a smart energy management system for a self-sustained home of any type situated in any geographical location with the availability of renewable energy sources like solar, etc. The purpose is mainly to highlight the importance and advantages of direct current (DC) homes with DC loads rather than a conventional alternating current (AC) home with both AC and DC loads. An attempt has been made to evolve a multi-agent coordinated control for the low voltage direct current (LVDC) smart home system.

LVDC supply systems with in situ power generation are providing an efficient solution for the energy needs of a DC smart home. The individual sub-systems of the LVDC system have their unique functions and priorities and hence require both coordinated and independent control. The entire DC smart home system is modeled in the Matlab and codes are implemented for each agent of the home. LVDC grid is operating either in battery connected mode or utility grid-connected mode, and the DC link voltage is held constant in both the cases. Energy imported from the utility grid is minimized by load shedding during the rectifier mode of the bidirectional converter. In addition, load shedding is also done when the battery is discharging to increase the discharge time of the battery. Load shedding is done on the basis of a fixed priority of loads. A 48 s simulation is performed on the Matlab model to bring out the 24-hour operation of the proposed system. Various modes are simulated and the corresponding actions of the agents are tested.

A new control strategy with agents for each sub-system of the LVDC system is presented. Each individual agent works in tandem with other agents and meets its own control imperatives without compromising the requirements of the overall system. Unlike the centralized control system, the proposed control strategy is a distributed control system. The control algorithm for each of the agents is developed, and the pseudo code is presented. The results of the simulation of the proposed scheme are presented to confirm the usefulness of the new control approach.

The multi-agent concept for an energy management system is less addressed and thus its potential for efficient home energy management is presented. The proposed multi-agent strategy for a complete DC smart home with exclusive DC loads is not done earlier and is reported for the first time. The success of this strategy can be extended to other DC micro-grid systems like telecom power systems, ships, aircraft, datacentres, server rooms, residential complexes and commercial malls.

The purpose of this paper is to illustrate and discuss the implications of the assessment and choice of electricity supply systems for rural communities of less than 500 inhabitants in Senegal. The paper is based on a study produced by Programme pour la Promotion de l'Electrification Rurale et l'Approvisionnement en Combustibles Domestiques, an advising body for the Senegalese Ministry of Energy and Mines.

The profitability index Taux d'Enrichissement en Capital is used as the main criterion for the economic evaluation of four technologies: diesel mini‐grids, photovoltaic, hybrid (pv‐diesel) generators, and solar home systems. Household demand is derived from real data of socio‐economic studies which serve as the basis for determining market segments defined by the distribution of the willingness to pay and the levels of service.

The simulations from nine demand cases show that high investment and/or operation expenditure create an insurmountable barrier given the limited payment capacity of rural populations, demonstrating that projects in this context are not profitable without subsidies. However, decentralised PV generation technologies are already demonstrated to be the least cost solution when the village lies further than 5.4 kilometers from the transmission grid.

This paper describes a planning path that could enable a faster implementation of rural electrification programs in remote areas considering three main elements, namely; willingness to pay, reduction of levels of supply service and support of communal management. However, the focus of the present work is mainly devoted to an analysis of the first two elements. Finally, the paper addresses the issue of how these technologies can be better implemented by national agencies and investors, with potential application outside of the Senegalese case study.

The purpose of this paper is to introduce a neural network‐based market agent, which develops optimal bidding strategies for a power generating company (Genco) in a day‐ahead electricity market.

The problem of finding optimal bidding strategy for a Genco is formulated as a two‐level optimization problem. At the top level, the Genco aims at maximizing its total daily profit, and at the bottom level, the independent system operator obtains the power dispatch quantity for each market participant with the objective of maximizing the social welfare. The neural network is trained using a particle swarm optimization (PSO) algorithm with the objective of maximizing daily profit for the Genco.

The effectiveness of the proposed approach is established through several case studies on the benchmark IEEE 30‐bus test system for the day‐ahead market, with an hourly clearing mechanism and dynamically changing demand profile. Both block bidding and linear supply function bidding are considered for the Gencos and the variation of optimal bidding strategy with the change in demand is investigated. The performance is also evaluated in the context of the Brazilian electricity market with real market data and compared with the other methods reported in the literature.

Strategic bidding is a peculiar phenomenon observed in an oligopolistic electricity market and has several implications on policy making and mechanism design. In this work, the transmission line constraints and demand side bidding are taken into account for a more realistic simulation.

To the best of the authors' knowledge, this paper has introduced, for the first time, a neural network‐based market agent to develop optimal bidding strategies of a Genco in an electricity market. Simulation results obtained from the IEEE 30‐bus test system and the Brazilian electricity market demonstrate the superiority of the proposed approach, as compared to the conventional PSO‐based method and the genetic fuzzy rule‐based system approach, respectively.

The purpose of this paper is to demonstrate that using advanced powertrain technologies can help outperform the state of the art in F1 and LeMans motor racing. By a careful choice and sizing of powertrain components coupled with an optimal energy management strategy, the conflicting requirements of high-performance and high-energy savings can be achieved.

Five main steps were performed. First, definition of requirements: basic performance requirements were defined based on research on the capabilities of Formula 1 race cars. Second, drive cycle generation: a drive cycle was created using these performance requirements as well as other necessary inputs such as the track layout of Circuit de la Sarthe, the drag coefficient, the tire specifications, and the mass of the vehicle. Third, selection of technology: the drive cycle was used to model the power requirements from the powertrain components of the series-hybrid topology. Fourth, lap time sensitivity analysis: the impact of certain design decisions on lap time was determined by the lap time sensitivity analysis. Fifth, modeling and optimization: the design involved building the optimal energy management strategy and comparing the performance of different powertrain component sizings.

Five different powertrain configurations were presented, and several tradeoffs between lap time and different parameters were discussed. The results showed that the fastest achievable lap time using the proposed configurations was 3 min 9 s. It was concluded that several car and component parameters have to be improved to decrease this lap time to the required 2 min 45 s, which is required to outperform F1 on LeMans.

This research shows the capabilities of advanced hybrid powertrain components and energy management strategies in motorsports, both in terms of performance and energy savings. The important factors affecting the performance of such a hybrid race car have been highlighted.

The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

This paper aims to introduce the efficiency improvement in the operating room (OR) of a local hospital using the integration of simulation and optimization.

Based on the simulation model, a Tabu search (TS) algorithm is developed as an optimizer for the meta-heuristic optimization method to find the optimum configuration of resources for the OR operation.

The computational efficiency is improved for the optimum search. Results show that 21 percent more patients can be processed compared to the existing operation. The average time stay of patients in the OR is reduced by 17 percent.

Limited resources considered in the model may limit the capacity of the proposed method, more resources including nurses, beds in post-operative units, and beds in inpatient wards will be included in the decision variables.

Long waiting lists in the OR lead to the low performance of healthcare systems. It is crucial to identify inefficiency and to improve the OR operation efficiently.

The TS-based heuristic optimizing method developed in this research shows the promise in time saving of the optimal solution search for the OR efficiency improvement.

This paper aims to propose a comprehensive methodology for setting up rural electrifications for indigenous villages with minimum budgets and the lowest possible cost of electricity (COE). The electricity accessibility of rural area in Malaysia is not fully covered and the cost of extending the grid to these areas can be high as RM 2.7m per km. Lack of vigorous policies and economic attraction of the rural areas are also the main barriers to rural electrification. Electricity is an essential element of economic activities and the lack of electricity exacerbates poverty and contributes to its perpetuation. Therefore, a hybrid standalone power system can be an alternative solution for the rural electrification. A hybrid standalone power system is studied to investigate the potential of the implementation and the budget required.

A site survey has been carried out in a village in Peninsular Malaysia, namely, Kampung Ulu Lawin Selatan. A standalone hybrid system is modeled in HOMER Pro software and the data collected from the selected site are used to obtain the system configuration with the lowest COE. The load following and cycle charging energy dispatch methods are compared to identify the optimal system configuration that yields the lowest COE. The diesel generator-only system is chosen as a benchmark for comparisons.

The results show that the hybrid system constituted from the diesel generator, photovoltaic (PV), micro-hydro and energy storage using the load following energy dispatch method yields the lowest COE of RM 0.519 per kWh. The COE of the hybrid system is 378 per cent lower than that of the diesel generator-only system. The lead-acid energy storage system (ESS) is able to reduce 40 per cent of COE as compared to the system without ESS.

The results indicate that the COE of the diesel-micro hydro-PV-ESS system with load following dispatch strategy is RM 0.519 per kWh, and this value is 35 per cent higher than the average electricity price in Malaysia. However, it is important to note that the costs of extending the grid to the rural area are not taken into account. If this cost is considered into the electricity price, then the standalone hybrid power system proposed by this study is still a competitive alternative for rural electrification.